{"article_number":"e64543","author":[{"id":"345D25EC-F248-11E8-B48F-1D18A9856A87","last_name":"Lagator","first_name":"Mato","full_name":"Lagator, Mato"},{"id":"35F0286E-F248-11E8-B48F-1D18A9856A87","last_name":"Sarikas","first_name":"Srdjan","full_name":"Sarikas, Srdjan"},{"full_name":"Steinrueck, Magdalena","last_name":"Steinrueck","first_name":"Magdalena"},{"first_name":"David","last_name":"Toledo-Aparicio","full_name":"Toledo-Aparicio, David"},{"full_name":"Bollback, Jonathan P","last_name":"Bollback","first_name":"Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4624-4612"},{"full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper"}],"month":"01","citation":{"short":"M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022).","apa":"Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., &#38; Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>","mla":"Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>, vol. 11, e64543, eLife Sciences Publications, 2022, doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>.","ieee":"M. Lagator <i>et al.</i>, “Predicting bacterial promoter function and evolution from random sequences,” <i>eLife</i>, vol. 11. eLife Sciences Publications, 2022.","ista":"Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543.","ama":"Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. <i>eLife</i>. 2022;11. doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>","chicago":"Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>. eLife Sciences Publications, 2022. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>."},"project":[{"name":"Selective Barriers to Horizontal Gene Transfer","grant_number":"648440","call_identifier":"H2020","_id":"2578D616-B435-11E9-9278-68D0E5697425"}],"title":"Predicting bacterial promoter function and evolution from random sequences","date_published":"2022-01-26T00:00:00Z","date_created":"2022-02-06T23:01:32Z","acknowledgement":"We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.","file_date_updated":"2022-02-07T07:14:09Z","scopus_import":"1","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"NiBa"}],"type":"journal_article","article_processing_charge":"No","ddc":["576"],"status":"public","file":[{"date_updated":"2022-02-07T07:14:09Z","file_id":"10739","date_created":"2022-02-07T07:14:09Z","relation":"main_file","file_size":5604343,"checksum":"decdcdf600ff51e9a9703b49ca114170","access_level":"open_access","file_name":"2022_ELife_Lagator.pdf","creator":"cchlebak","success":1,"content_type":"application/pdf"}],"_id":"10736","volume":11,"publication_status":"published","quality_controlled":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"eLife Sciences Publications","abstract":[{"lang":"eng","text":"Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought."}],"oa":1,"external_id":{"pmid":["35080492"],"isi":["000751104400001"]},"has_accepted_license":"1","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"oa_version":"Published Version","license":"https://creativecommons.org/licenses/by/4.0/","pmid":1,"isi":1,"publication":"eLife","ec_funded":1,"doi":"10.7554/eLife.64543","date_updated":"2023-08-02T14:09:02Z","day":"26","language":[{"iso":"eng"}],"year":"2022","intvolume":"        11","article_type":"original","publication_identifier":{"eissn":["2050-084X"]}}